Event Spectroscopy: Event-based Multispectral and Depth Sensing using Structured Light

• URL: http://arxiv.org/abs/2509.06741v1
• Date: Mon, 08 Sep 2025 14:34:55 GMT
• Title: Event Spectroscopy: Event-based Multispectral and Depth Sensing using Structured Light
• Authors: Christian Geckeler, Niklas Neugebauer, Manasi Muglikar, Davide Scaramuzza, Stefano Mintchev,
• Abstract summary: We present a novel event spectroscopy system that simultaneously enables high-resolution, low-latency depth reconstruction and multispectral imaging.<n>A portable version limited to RGB (3 wavelengths) is used to collect real-world depth and spectral data from a Masoala Rainforest.<n>Our results show that adding depth (available at no extra effort with our setup) to material differentiation improves the accuracy by over $30%$ compared to color-only method.
• Score: 20.931791276832104
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed in forest environments for tasks such as environmental monitoring and search and rescue, which require safe navigation through dense foliage and precise data collection. Traditional sensing approaches, including passive multispectral and RGB imaging, suffer from latency, poor depth resolution, and strong dependence on ambient light - especially under forest canopies. In this work, we present a novel event spectroscopy system that simultaneously enables high-resolution, low-latency depth reconstruction and multispectral imaging using a single sensor. Depth is reconstructed using structured light, and by modulating the wavelength of the projected structured light, our system captures spectral information in controlled bands between 650 nm and 850 nm. We demonstrate up to $60\%$ improvement in RMSE over commercial depth sensors and validate the spectral accuracy against a reference spectrometer and commercial multispectral cameras, demonstrating comparable performance. A portable version limited to RGB (3 wavelengths) is used to collect real-world depth and spectral data from a Masoala Rainforest. We demonstrate the use of this prototype for color image reconstruction and material differentiation between leaves and branches using spectral and depth data. Our results show that adding depth (available at no extra effort with our setup) to material differentiation improves the accuracy by over $30\%$ compared to color-only method. Our system, tested in both lab and real-world rainforest environments, shows strong performance in depth estimation, RGB reconstruction, and material differentiation - paving the way for lightweight, integrated, and robust UAV perception and data collection in complex natural environments.

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